Row 63481

Row ID: 63481 | Dataset Entry | Axioma AXP Content Repository

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> Just as a smell test, it couldn't have been back prop because children learn language(s) without being exposed to nearly as much data (in terms of the diversity of words and sentences) as most statistical learning rules seem to require.

One counterargument is that we have an innate universal grammar, so it's not necessary to learn everything from the data. But poverty of the stimulus arguments have seen better days.

Another counterargument is that there are many types of learning in the brain, so saying that X can't be done with backprop doesn't preclude that Y or Z can be done with backprop. There is certainly evidence that the brain implements different classes of learning (supervised, unsupervised, and RL) in different areas of the cortex.

There's also the argument that language is not learned through spoken language alone but through embodiment, including multisensory perception (I have millions of different visual perceptual inputs for a given word, even if I've only heard that word a few times) and agency (the ability to simulate true experiments with causality).

I'm not suggesting that backprop is the answer - there are many reasons to think it isn't - but poverty of the stimulus is a weak one imo compared to other properties that backprop struggles with (one-shot learning, deep belief nets that resemble human causal models)

FieldValue
text > Just as a smell test, it couldn't have been back prop because children learn language(s) without being exposed to nearly as much data (in terms of the diversity of words and sentences) as most statistical learning rules seem to require. One counterargument is that we have an innate universal grammar, so it's not necessary to learn everything from the data. But poverty of the stimulus arguments have seen better days. Another counterargument is that there are many types of learning in the brai…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-23
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Raw Record

{
  "text": "> Just as a smell test, it couldn't have been back prop because children learn language(s) without being exposed to nearly as much data (in terms of the diversity of words and sentences) as most statistical learning rules seem to require.\n\nOne counterargument is that we have an innate universal grammar, so it's not necessary to learn everything from the data. But poverty of the stimulus arguments have seen better days.\n\nAnother counterargument is that there are many types of learning in the brain, so saying that X can't be done with backprop doesn't preclude that Y or Z can be done with backprop. There is certainly evidence that the brain implements different classes of learning (supervised, unsupervised, and RL) in different areas of the cortex.\n\nThere's also the argument that language is not learned through spoken language alone but through embodiment, including multisensory perception (I have millions of different visual perceptual inputs for a given word, even if I've only heard that word a few times) and agency (the ability to simulate true experiments with causality).\n\nI'm not suggesting that backprop is the answer - there are many reasons to think it isn't - but poverty of the stimulus is a weak one imo compared to other properties that backprop struggles with (one-shot learning, deep belief nets that resemble human causal models)",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-23",
  "username_encoded": "Z0FBQUFBQm5Lak1hMTVCcm1XMWhLa25hc1hZU19Nd0M2Q29uRmFUTDZGWERSaXpOX1lLWUNWWUhkaDBFdnUzWFFQNDZSejZPdlprYnQ5RldtWXdvRkpmLTRNLWxEZHVsTnc9PQ==",
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}
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